



import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

	from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
	a      :  List[Any]			     =			False

if is_vision_available():
	from PIL import Image

	from transformers import PixaStructImageProcessor







class 				_a				(   unittest.TestCase					):

			def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=7,   SCREAMING_SNAKE_CASE_=3,   SCREAMING_SNAKE_CASE_=18,   SCREAMING_SNAKE_CASE_=30,   SCREAMING_SNAKE_CASE_=400,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=None,   )       ->   Dict:
						UpperCAmelCase_:		Optional[Any]							      =     size if size is not None else {"""height""": 20, """width""": 20}
						UpperCAmelCase_:		str							      =     parent
						UpperCAmelCase_:		List[Any]							      =     batch_size
						UpperCAmelCase_:		Dict							      =     num_channels
						UpperCAmelCase_:		Dict							      =     image_size
						UpperCAmelCase_:		Optional[Any]							      =     min_resolution
						UpperCAmelCase_:		Dict							      =     max_resolution
						UpperCAmelCase_:		Dict							      =     size
						UpperCAmelCase_:		Tuple							      =     do_normalize
						UpperCAmelCase_:		Tuple							      =     do_convert_rgb
						UpperCAmelCase_:		Tuple							      =     [512, 1024, 2048, 4096]
						UpperCAmelCase_:		Optional[Any]							      =     patch_size if patch_size is not None else {"""height""": 16, """width""": 16}

			def      __snake_case       (self					)       ->   Dict:
						return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}

			def      __snake_case       (self					)       ->   List[Any]:
						UpperCAmelCase_:		Union[str, Any]							      =     """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
						UpperCAmelCase_:		Any							      =     Image.open(requests.get(SCREAMING_SNAKE_CASE_,   stream=SCREAMING_SNAKE_CASE_					).raw					).convert("""RGB"""					)
						return raw_image







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11 ,		reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.''' ,		)
@require_torch
@require_vision
class 				_a				(   _lowerCAmelCase ,		unittest.TestCase					):
			A      		=	PixaStructImageProcessor if is_vision_available() else None

			def      __snake_case       (self					)       ->   Dict:
						UpperCAmelCase_:		int							      =     PixaStructImageProcessingTester(self					)

			@property
			def      __snake_case       (self					)       ->   List[str]:
						return self.image_processor_tester.prepare_image_processor_dict()

			def      __snake_case       (self					)       ->   Optional[int]:
						UpperCAmelCase_:		List[Any]							      =     self.image_processing_class(**self.image_processor_dict					)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,   """do_normalize"""					)					)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,   """do_convert_rgb"""					)					)

			def      __snake_case       (self					)       ->   Union[str, Any]:
						UpperCAmelCase_:		List[Any]							      =     self.image_processor_tester.prepare_dummy_image()

						UpperCAmelCase_:		Optional[int]							      =     self.image_processing_class(**self.image_processor_dict					)
						UpperCAmelCase_:		Tuple							      =     2048

						UpperCAmelCase_:		Any							      =     image_processor(SCREAMING_SNAKE_CASE_,   return_tensors="""pt""",   max_patches=SCREAMING_SNAKE_CASE_					)
						self.assertTrue(torch.allclose(inputs.flattened_patches.mean(),   torch.tensor(0.0_6_0_6					),   atol=1E-3,   rtol=1E-3					)					)

			def      __snake_case       (self					)       ->   Optional[Any]:
						# Initialize image_processor
						UpperCAmelCase_:		Tuple							      =     self.image_processing_class(**self.image_processor_dict					)
						# create random PIL images
						UpperCAmelCase_:		Tuple							      =     prepare_image_inputs(self.image_processor_tester,   equal_resolution=SCREAMING_SNAKE_CASE_					)
						for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_,   Image.Image					)

						# Test not batched input
						UpperCAmelCase_:		List[Any]							      =     (
						    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
						    * self.image_processor_tester.num_channels
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									UpperCAmelCase_:		List[str]							      =     image_processor(
									    image_inputs[0],   return_tensors="""pt""",   max_patches=SCREAMING_SNAKE_CASE_					).flattened_patches
									self.assertEqual(
									    encoded_images.shape,   (1, max_patch, expected_hidden_dim),   )

									# Test batched
									UpperCAmelCase_:		str							      =     image_processor(
									    SCREAMING_SNAKE_CASE_,   return_tensors="""pt""",   max_patches=SCREAMING_SNAKE_CASE_					).flattened_patches
									self.assertEqual(
									    encoded_images.shape,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),   )

			def      __snake_case       (self					)       ->   Optional[int]:
						# Initialize image_processor
						UpperCAmelCase_:		List[Any]							      =     self.image_processing_class(**self.image_processor_dict					)
						# create random PIL images
						UpperCAmelCase_:		Union[str, Any]							      =     prepare_image_inputs(self.image_processor_tester,   equal_resolution=SCREAMING_SNAKE_CASE_					)
						for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_,   Image.Image					)

						# Test not batched input
						UpperCAmelCase_:		List[str]							      =     (
						    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
						    * self.image_processor_tester.num_channels
						) + 2

						UpperCAmelCase_:		Union[str, Any]							      =     True

						for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									with self.assertRaises(SCREAMING_SNAKE_CASE_					):
												UpperCAmelCase_:		Dict							      =     image_processor(
												    image_inputs[0],   return_tensors="""pt""",   max_patches=SCREAMING_SNAKE_CASE_					).flattened_patches

									UpperCAmelCase_:		int							      =     """Hello"""

									UpperCAmelCase_:		Dict							      =     image_processor(
									    image_inputs[0],   return_tensors="""pt""",   max_patches=SCREAMING_SNAKE_CASE_,   header_text=SCREAMING_SNAKE_CASE_					).flattened_patches
									self.assertEqual(
									    encoded_images.shape,   (1, max_patch, expected_hidden_dim),   )

									# Test batched
									UpperCAmelCase_:		Any							      =     image_processor(
									    SCREAMING_SNAKE_CASE_,   return_tensors="""pt""",   max_patches=SCREAMING_SNAKE_CASE_,   header_text=SCREAMING_SNAKE_CASE_					).flattened_patches
									self.assertEqual(
									    encoded_images.shape,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),   )

			def      __snake_case       (self					)       ->   List[Any]:
						# Initialize image_processor
						UpperCAmelCase_:		List[str]							      =     self.image_processing_class(**self.image_processor_dict					)
						# create random numpy tensors
						UpperCAmelCase_:		Union[str, Any]							      =     prepare_image_inputs(self.image_processor_tester,   equal_resolution=SCREAMING_SNAKE_CASE_,   numpify=SCREAMING_SNAKE_CASE_					)
						for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_,   np.ndarray					)

						UpperCAmelCase_:		List[str]							      =     (
						    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
						    * self.image_processor_tester.num_channels
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									UpperCAmelCase_:		List[str]							      =     image_processor(
									    image_inputs[0],   return_tensors="""pt""",   max_patches=SCREAMING_SNAKE_CASE_					).flattened_patches
									self.assertEqual(
									    encoded_images.shape,   (1, max_patch, expected_hidden_dim),   )

									# Test batched
									UpperCAmelCase_:		Any							      =     image_processor(
									    SCREAMING_SNAKE_CASE_,   return_tensors="""pt""",   max_patches=SCREAMING_SNAKE_CASE_					).flattened_patches
									self.assertEqual(
									    encoded_images.shape,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),   )

			def      __snake_case       (self					)       ->   Any:
						# Initialize image_processor
						UpperCAmelCase_:		int							      =     self.image_processing_class(**self.image_processor_dict					)
						# create random PyTorch tensors
						UpperCAmelCase_:		Dict							      =     prepare_image_inputs(self.image_processor_tester,   equal_resolution=SCREAMING_SNAKE_CASE_,   torchify=SCREAMING_SNAKE_CASE_					)
						for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_,   torch.Tensor					)

						# Test not batched input
						UpperCAmelCase_:		Tuple							      =     (
						    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
						    * self.image_processor_tester.num_channels
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									UpperCAmelCase_:		Union[str, Any]							      =     image_processor(
									    image_inputs[0],   return_tensors="""pt""",   max_patches=SCREAMING_SNAKE_CASE_					).flattened_patches
									self.assertEqual(
									    encoded_images.shape,   (1, max_patch, expected_hidden_dim),   )

									# Test batched
									UpperCAmelCase_:		Any							      =     image_processor(
									    SCREAMING_SNAKE_CASE_,   return_tensors="""pt""",   max_patches=SCREAMING_SNAKE_CASE_					).flattened_patches
									self.assertEqual(
									    encoded_images.shape,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),   )







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11 ,		reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.''' ,		)
@require_torch
@require_vision
class 				_a				(   _lowerCAmelCase ,		unittest.TestCase					):
			A      		=	PixaStructImageProcessor if is_vision_available() else None

			def      __snake_case       (self					)       ->   Dict:
						UpperCAmelCase_:		Optional[Any]							      =     PixaStructImageProcessingTester(self,   num_channels=4					)
						UpperCAmelCase_:		List[str]							      =     3

			@property
			def      __snake_case       (self					)       ->   int:
						return self.image_processor_tester.prepare_image_processor_dict()

			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_:		Optional[int]							      =     self.image_processing_class(**self.image_processor_dict					)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,   """do_normalize"""					)					)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,   """do_convert_rgb"""					)					)

			def      __snake_case       (self					)       ->   Optional[Any]:
						# Initialize image_processor
						UpperCAmelCase_:		Union[str, Any]							      =     self.image_processing_class(**self.image_processor_dict					)
						# create random PIL images
						UpperCAmelCase_:		Tuple							      =     prepare_image_inputs(self.image_processor_tester,   equal_resolution=SCREAMING_SNAKE_CASE_					)
						for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_,   Image.Image					)

						# Test not batched input
						UpperCAmelCase_:		Tuple							      =     (
						    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
						    * (self.image_processor_tester.num_channels - 1)
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									UpperCAmelCase_:		int							      =     image_processor(
									    image_inputs[0],   return_tensors="""pt""",   max_patches=SCREAMING_SNAKE_CASE_					).flattened_patches
									self.assertEqual(
									    encoded_images.shape,   (1, max_patch, expected_hidden_dim),   )

									# Test batched
									UpperCAmelCase_:		List[Any]							      =     image_processor(
									    SCREAMING_SNAKE_CASE_,   return_tensors="""pt""",   max_patches=SCREAMING_SNAKE_CASE_					).flattened_patches
									self.assertEqual(
									    encoded_images.shape,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),   )











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a      :  Optional[Any]			     =			{
    'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
    'tokenization_mvp': ['MvpTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  str			     =			['MvpTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  Optional[Any]			     =			[
	    'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'MvpForCausalLM',
	    'MvpForConditionalGeneration',
	    'MvpForQuestionAnswering',
	    'MvpForSequenceClassification',
	    'MvpModel',
	    'MvpPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
	from .tokenization_mvp import MvpTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mvp_fast import MvpTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mvp import (
		    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MvpForCausalLM,
		    MvpForConditionalGeneration,
		    MvpForQuestionAnswering,
		    MvpForSequenceClassification,
		    MvpModel,
		    MvpPreTrainedModel,
		)

else:
	import sys

	a      :  Optional[Any]			     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






